The K-means clustering algorithm is widely applied in various clustering tasks due to its high computational efficiency and simple implementation. However, its performance significantly deteriorates when dealing with non-convex structures, fuzzy boun...
Environmental, Social, and Governance (ESG) reports have become essential tools for enterprises to showcase their commitment to sustainable development and social responsibility. However, discrepancies persist regarding the criteria, assessments, and...
Fuzzy control theory has been extensively used in the construction of complex fuzzy inference systems. However, we argue that existing fuzzy control technologies focus mainly on the single-source fuzzy information system, disregarding the complementa...
With the rapid development of sensors and other devices, precise control for the generation of new energy, especially in the context of highly stochastic wind power generation, has been strongly supported. However, large-scale wind farm grid connecti...
Diabetes mellitus is a major public health concern, creating a need for reliable computational approaches for diabetes status classification and risk-oriented clinical assessment. Conventional machine learning models can capture predictive relationsh...
(1) Objectives: Reliability is one of the major aspects for enhancing the operability, reusability, maintainability, and quality of a system. A software component is an independent entity that deploys to form a functional system (CBSS). The component...
Time series forecasting can be a complicated problem when the underlying process shows high degree of complex nonlinear behavior. In some domains, such as financial data, processing related time-series jointly can have significant benefits. This pape...
Clustering is more popular than the expert knowledge approach in Interval Fuzzy Type-2 membership function construction because it can construct membership function automatically with less time consumption. Most research proposed a two-fuzzifier fuzz...
In this paper, the optimal designs of type-1 and interval type-2 fuzzy systems for the classification of the heart rate level are presented. The contribution of this work is a proposed approach for achieving the optimal design of interval type-2 fuzz...
Breeding in large-scale marine aquafarms faces many challenges in terms of precise feeding, including real-time decisions as to the precise feeding amount, along with disturbances caused by the feeding speed and the moving speed of feeding equipment....
This paper presents a method to dynamically model Type-1 fuzzy inference systems using a Competitive Neural Network. The aim is to exploit the potential of Competitive Neural Networks and fuzzy logic systems to generate an intelligent hybrid model wi...
Fuzzy logic is an approach that reflects human thinking and decision making by handling uncertainty and vagueness using fuzzy membership functions. When a human is engaged in the design of a fuzzy system, symmetric properties are naturally preferred....
Pastures are the primary food source for cattle, yet their productivity is often limited by management practices and water scarcity. In this context, approaches capable of representing nonlinear relationships and handling uncertainties can support su...
This paper proposes a land cover classification method that combines a Gaussian regression model (GRM) with an interval type-2 fuzzy neural network (IT2FNN) model as a classification decision model. Problems such as the increase in the complexity of...
The theory of belief functions has been extensively utilized in many practical applications involving decision making. One such application is the classification of target based on the pieces of information extracted from the individual attributes de...
Latent class choice models (LCCMs) become increasingly complex when class membership depends on covariates of mixed measurement type, since the standard practice of fitting each covariate block with a maximally flexible density (e.g., a negative bino...
Since the fuzzy local information C-means (FLICM) segmentation algorithm cannot take into account the impact of different features on clustering segmentation results, a local fuzzy clustering segmentation algorithm based on a feature selection Gaussi...
The use of artificial intelligence techniques such as fuzzy logic, neural networks and evolutionary computation is currently very important in medicine to be able to provide an effective and timely diagnosis. The use of fuzzy logic allows to design f...
Laos Pavement Management System (PMS) manages 7700 km of National Roads (NRs) and estimates their Maintenance and Rehabilitation (MR) needs based on assessing pavement roughness conditions. This research aims to develop two International Roughness In...
Accurate dynamic flow statistics for trackless vehicles are critical for efficiently scheduling trackless transportation systems in underground mining. However, traditional discrete time-point methods suffer from “time membership discontinuity&...
An important problem in machine learning is that, when using more than two labels, it is very difficult to construct and optimize a group of learning functions that are still useful when the prior distribution of instances is changed. To resolve this...
Passive electronic intelligence (ELINT) and electronic warfare receivers must deinterleave pulse descriptor word (PDW) streams in real time: each pulse is labelled on arrival, unsupervised and irrevocably, at bounded cost. Frequency- and PRI-agile em...
Dempster–Shafer evidence theory is widely used in many soft sensors data fusion systems on account of its good performance for handling the uncertainty information of soft sensors. However, how to determine basic belief assignment (BBA) is still an o...
An Adaptive Neuro-Fuzzy Inference System (ANFIS) framework for paediatric wrist injury classification (fracture versus sprain) was developed utilising infrared thermography (IRT). ANFIS combines artificial neural network (ANN) learning with interpret...
Modeling under uncertainty remains a fundamental challenge in engineering and nonlinear system identification, particularly when both interpretability and computational efficiency are required. Interval type-2 adaptive neuro-fuzzy inference systems (...
This research work envisages addressing fuzzy adjustment of parameters into a hybrid optimization algorithm for solving mathematical benchmark function problems. The problem of benchmark mathematical functions consists of finding the minimal values....
A distribution static compensator (DSTATCOM) is proposed in this study to improve the power quality, which includes the total harmonic distortion (THD) of the grid current and power factor (PF), of a mini grid with nonlinear and linear inductive load...
This study aims to characterize the emissions of ammonia (NH3) and methane (CH4) from naturally ventilated dairy barns located in cold regions during the winter season, thereby providing a scientific basis for optimizing dairy barn environmental mana...
Piezoelectric actuators (PEAs) have been widely used in aerospace, electronic communication and other high-accuracy manufacturing fields because of their high precision, low power consumption, fast response, and high resolution. However, piezoelectri...
This paper presents the design and simulation of improved circuits of Fuzzifier and capacitance to voltage (C2V) converter. The Fuzzifier circuit is designed based on analog advantages such as low die area, high accuracy, and simplicity which are add...
Jute is a bio-degradable, agro-renewable, and widely available lingo cellulosic fiber having high tensile strength and initial modulus, moisture regain, good sound, and heat insulation properties. For these unique properties and eco-friendly nature o...
This paper describes a comparable study on the influence of frequency-sweep on the discrete and continuous phase distributions associated with pulse excitations for a double-lambda atomic system in alkali-metal vapours with a hyperfine structure. The...
Floods are potential natural disasters that might disrupt human activities, resulting in severe losses of life and property in a region. Excessive rainfall is one of the reasons for flooding, especially in the downstream areas of a catchment. Because...
The article portrays an adaptive control paradigm for the swift response of a solid-oxide fuel cell (SOFC) in a grid-connected microgrid. The control scheme is based on an adaptive feedback-linearization-embedded fully recurrent NeuroFuzzy Laguerre w...
Developing countries all over the world face numerous difficulties with regard to food security. The purpose of this research is to develop a new approach for evaluating wheat’s suitability for cultivation. To this end, geographical information...
Studying and analyzing the challenges that the renewable energy sector faces can help evaluate the risks and improve the planning. This research is done by considering the challenges in the implementation of sustainable generation of electricity thro...
This study investigates the effect of active control on a quarter-vehicle suspension system. The car suspension system was modeled using the Lagrange–Euler method. The linear quadratic Gaussian (LQG) and fuzzy linear quadratic Gaussian (FLQG) c...
Remote sensing change detection technology is widely used in land-use monitoring, urban planning, and disaster assessment. However, during imaging and transmission, bi-temporal remote sensing images are vulnerable to Gaussian noise, which makes it di...
The Hopfield Recurrent Neural Network (HRNN) is a single-point descent metaheuristic that uses a single potential solution to explore the search space of optimization problems, whose constraints and objective function are aggregated into a typical en...
Image de-noising is an important scheme that makes an image visually prominent and obtains enough useful information to produce a clear image. Many applications have been developed for effective noise suppression that produce good image quality. This...
Recently, Transformers have made significant progress in the joint classification task of HSI and LiDAR due to their efficient modeling of long-range dependencies and adaptive feature learning mechanisms. However, existing methods face two key challe...
Wind–photovoltaic (PV) modular-multilevel-converter-based high-voltage direct-current (MMC-HVDC) collection systems have limited inherent inertia and are vulnerable to frequency disturbances caused by renewable power fluctuations. Moreover, fre...
Fast charging of lithium-ion battery packs involves a compromise between charging speed, temperature rise, and aggressive current profiles that may accelerate battery degradation. This paper presents a current-stress-aware fuzzy logic control framewo...
In 20th century, many countries have liberalized their electricity market. This power markets liberalization has directed generation companies as well as wholesale buyers to undertake a greater intense risk exposure compared to the old centralized fr...
This paper presents a fuzzy logic control strategy for synchronising the vertical lifting and positioning of a multi-actuator hydraulic system designed for a 360-ton movable platform. The primary focus is on achieving precise actuator movement coordi...
In this work, the possibilistic fuzzy C-means clustering artificial bee colony support vector machine (PFCM-ABC-SVM) method is proposed and applied for the fault diagnosis of a polymer electrolyte membrane (PEM) fuel cell system. The innovation of th...
Optimization by refinement of linguistic contexts produced from an output variable in the construction of an incremental granular model (IGM) is presented herein. In contrast to the conventional learning method using the backpropagation algorithm, we...
Blood vessels are harmed in diabetic retinopathy (DR), a condition that impairs vision. Using modern healthcare research and technology, artificial intelligence and processing units are used to aid in the diagnosis of this syndrome and the study of d...
Renal cancer is a serious and common type of cancer affecting old ages. The growth of such type of cancer can be stopped by detecting it before it reaches advanced or end-stage. Hence, renal cancer must be identified and diagnosed in the initial stag...
Support vector machines are supervised learning models which are capable of classifying data and measuring regression by means of a learning algorithm. If data are linearly separable, a conventional linear kernel is used to classify them. Otherwise,...
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